Abstract Nowadays, the most notable uncertainty for an electricity utility lies in the electrical demand and generation in power systems. Demand response (DR) accomplishment due to the home appliances energy management has acquired considerable attention for the reliable and cost-optimized power grid. The optimum schedule of home appliances is a challenging task due to uncertain electricity prices and consumption patterns. Given this background, an innovative home appliance scheduling (IHAS) framework is proposed based on the fusion of the grey wolf and crow search optimization (GWCSO) algorithm. Using the proposed technique, the cost of electricity reduction, users-comfort maximization, and peak to average ratio reduction is analyzed for home appliances in the presence of real-time price signals (RTPS). The proposed optimization algorithm is also employed for Air Conditioners (ACs) scheduling and end-users comfort maximization in its usage due to the high percentage of ACs load. Simulation results indicate that the proposed GWCSO approach is robust, computationally efficient, and outperforms conventional ones in terms of electricity cost, peak to average ratio, and it also demonstrate that there is a trade-off between users’ comfort considering appliances waiting time and electricity cost. Thus, it can provide guidance for precise electricity consumption predictions and different DR actions.

中文翻译：

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考虑终端用户舒适度的基于 GWCSO 的最优家电调度需求响应

摘要 如今，电力公司最显着的不确定性在于电力系统的电力需求和发电。由于家用电器能源管理的需求响应 (DR) 成就，可靠且成本优化的电网引起了相当大的关注。由于电价和消费模式的不确定性，家用电器的最佳时间表是一项具有挑战性的任务。在此背景下，基于灰狼和乌鸦搜索优化（GWCSO）算法的融合，提出了一种创新的家电调度（IHAS）框架。使用所提出的技术，在存在实时价格信号 (RTPS) 的情况下，分析了家用电器的电力减少成本、用户舒适度最大化和峰均比降低。由于空调负载的高百分比，所提出的优化算法还用于空调 (AC) 调度和最终用户在其使用中的舒适度最大化。仿真结果表明，所提出的 GWCSO 方法稳健、计算效率高，并且在电力成本、峰均比方面优于传统方法，并且还表明在考虑电器等待时间和电力的用户舒适度之间存在权衡成本。因此，它可以为精确的电力消耗预测和不同的 DR 行动提供指导。并在电费、峰均比等方面优于传统电费，同时也证明了考虑家电等待时间和电费的用户舒适度之间存在权衡。因此，它可以为精确的电力消耗预测和不同的 DR 行动提供指导。并在电费、峰均比等方面优于传统电费，同时也证明了考虑家电等待时间和电费的用户舒适度之间存在权衡。因此，它可以为精确的电力消耗预测和不同的 DR 行动提供指导。